On the road to explainable AI in drug-drug interactions prediction: A systematic review

Co-administration of two or more drugs simultaneously can result in adverse drug reactions. Identifying drug-drug interactions (DDIs) is necessary, especially for drug development and for repurposing old drugs. DDI prediction can be viewed as a matrix completion task, for which matrix factorization (MF) appears as a suitable solution. This paper presents a novel Graph Regularized Probabilistic Matrix Factorization (GRPMF) method, which incorporates expert knowledge through a novel graph-based regularization strategy within an MF framework. An efficient and sounded optimization algorithm is proposed to solve the resulting non-convex problem in an alternating fashion. The performance of the proposed method is evaluated through the DrugBank dataset, and comparisons are provided against state-of-the-art techniques. The results demonstrate the superior performance of GRPMF when compared to its counterparts.

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“…The expression for the proximity operator of each term g ij , defined in (20), depends if (i, j) ∈ E U or not.…”

Section: B Optimization Algorithmmentioning

confidence: 99%

“…Due to the black-box nature of the DL models, some work has been done on seeking for explainable DL-based DDI techniques. A comprehensive review of the explainable AI-based techniques to promote the trust of AI models for the critical task of DDI prediction is presented in [20].…”

Section: Related Workmentioning

confidence: 99%

Graph Regularized Probabilistic Matrix Factorization for Drug-Drug Interactions Prediction

Jain

Chouzenoux

Kumar

et al. 2022

Preprint

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“…AI models in healthcare are not limited to epidemics and are utilised for various applications including drug-drug interactions [ 14 ] and the identification of salient sites in epigenetics [ 15 ].…”

Section: Introductionmentioning

confidence: 99%

Big data- and artificial intelligence-based hot-spot analysis of COVID-19: Gauteng, South Africa, as a case study

Lieberman

Kong

Gusinow

et al. 2023

BMC Med Inform Decis Mak

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The coronavirus disease 2019 (COVID-19) has developed into a pandemic. Data-driven techniques can be used to inform and guide public health decision- and policy-makers. In generalizing the spread of a virus over a large area, such as a province, it must be assumed that the transmission occurs as a stochastic process. It is therefore very difficult for policy and decision makers to understand and visualize the location specific dynamics of the virus on a more granular level. A primary concern is exposing local virus hot-spots, in order to inform and implement non-pharmaceutical interventions. A hot-spot is defined as an area experiencing exponential growth relative to the generalised growth of the pandemic. This paper uses the first and second waves of the COVID-19 epidemic in Gauteng Province, South Africa, as a case study. The study aims provide a data-driven methodology and comprehensive case study to expose location specific virus dynamics within a given area. The methodology uses an unsupervised Gaussian Mixture model to cluster cases at a desired granularity. This is combined with an epidemiological analysis to quantify each cluster’s severity, progression and whether it can be defined as a hot-spot.

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“…It has been shown that machine learning (ML) models could improve risk prediction in various diseases [ 8 – 14 ], and drug-drug interactions [ 15 , 16 ]. Results indicate that ML models have advantages compared to conventional logistic or linear regression by considering high-order, non-linear interactions, yielding more stable predictions.…”

Section: Introductionmentioning

confidence: 99%

Machine learning model identifies aggressive acute pancreatitis within 48 h of admission: a large retrospective study

Liu

Wang

et al. 2022

BMC Med Inform Decis Mak

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Background Acute pancreatitis (AP) with critical illness is linked to increased morbidity and mortality. Current risk scores to identify high-risk AP patients have certain limitations. Objective To develop and validate a machine learning tool within 48 h after admission for predicting which patients with AP will develop critical illness based on ubiquitously available clinical, laboratory, and radiologic variables. Methods 5460 AP patients were enrolled. Clinical, laboratory, and imaging variables were collected within 48 h after hospital admission. Least Absolute Shrinkage Selection Operator with bootstrap method was employed to select the most informative variables. Five different machine learning models were constructed to predictive likelihood of critical illness, and the optimal model (APCU) was selected. External cohort was used to validate APCU. APCU and other risk scores were compared using multivariate analysis. Models were evaluated by area under the curve (AUC). The decision curve analysis was employed to evaluate the standardized net benefit. Results Xgboost was constructed and selected as APCU, involving age, comorbid disease, mental status, pulmonary infiltrates, procalcitonin (PCT), neutrophil percentage (Neu%), ALT/AST, ratio of albumin and globulin, cholinesterase, Urea, Glu, AST and serum total cholesterol. The APCU performed excellently in discriminating AP risk in internal cohort (AUC = 0.95) and external cohort (AUC = 0.873). The APCU was significant for biliogenic AP (OR = 4.25 [2.08–8.72], P < 0.001), alcoholic AP (OR = 3.60 [1.67–7.72], P = 0.001), hyperlipidemic AP (OR = 2.63 [1.28–5.37], P = 0.008) and tumor AP (OR = 4.57 [2.14–9.72], P < 0.001). APCU yielded the highest clinical net benefit, comparatively. Conclusion Machine learning tool based on ubiquitously available clinical variables accurately predicts the development of AP, optimizing the management of AP.

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On the road to explainable AI in drug-drug interactions prediction: A systematic review

Cited by 81 publications

References 112 publications

Graph Regularized Probabilistic Matrix Factorization for Drug-Drug Interactions Prediction

Graph Regularized Probabilistic Matrix Factorization for Drug-Drug Interactions Prediction

Big data- and artificial intelligence-based hot-spot analysis of COVID-19: Gauteng, South Africa, as a case study

Machine learning model identifies aggressive acute pancreatitis within 48 h of admission: a large retrospective study

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